# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
"""
For each operation or module, there are two functions.
One is given output shape, infer its input shape and initialization parameters (e.g., weight's shape)
The other is given input shape, infer its output shape and initialization parameters (e.g., weight's shape)
"""

import logging
import torch

_logger = logging.getLogger(__name__)

conv_prune_dim = -1


def set_conv_prune_dim(dim):
    """
    Parameters:
    dim: int
        0: filter pruning
        1: channel pruning
    """
    global conv_prune_dim
    conv_prune_dim = dim


class CoarseMask:
    """
    Coarse grained mask for a given tensor, here tensor could be weights,
    input tensor, or output tensor
    """

    def __init__(self, num_dim):
        """
        Parameters
        ----------
        num_dim : int
            The number of dimensions of the tensor that will be masked
        """
        self.mask_index = [None for _ in range(num_dim)]

    def add_index_mask(self, dim, index):
        """
        Add mask for the specified dimension

        Parameters
        ----------
        dim : int
            The dimension to add mask
        index : tensor
            The mask for this dimension, its a 1 dimension tensor which specifies
            the index of the elements that are not pruned
        """
        self.mask_index[dim] = index

    @staticmethod
    def merge_index(index_a, index_b):
        """
        Parameters
        ----------
        index_a : tensor
            One index (1-dimension) tensor
        index_b : tensor
            The other index (1-dimension) tensor

        Returns
        -------
        tensor
            The merged index (1-dimension) tensor
            Note that: the output tensor will be moved
            to the same device as index_a.
        """
        device = index_a.device
        s = set()
        for num in index_a.tolist():
            # we need to transfer the tensor to list here
            # first, directly traversing the tensor by for
            # loop will return the list of tensor(x) object,
            # even the value are the same, but they are different
            # tensor objects, so the set will contains multiple
            # tensor objects that has the same value. For example
            # for num in torch.ones(2):
            #   s.add(num)
            # s will be {tensor(1), tensor(1)}
            s.add(num)
        for num in index_b.tolist():
            s.add(num)
        # move the output tensor to the same device with index_a
        return torch.tensor(sorted(s)).to(device)  # pylint: disable=not-callable

    def merge(self, cmask):
        """
        Merge another CoarseMask

        Parameters
        ----------
        cmask : CoarseMask
            Another CoarseMask to merge

        Returns
        -------
        list
            The member variable ```mask_index```
        """
        assert isinstance(cmask, CoarseMask)
        assert len(self.mask_index) == len(cmask.mask_index), \
            "Only masks with the same number of dimensions can be merged"
        for i, index in enumerate(self.mask_index):
            if index is None:
                self.mask_index[i] = cmask.mask_index[i]
            elif cmask.mask_index[i] is not None:
                self.mask_index[i] = CoarseMask.merge_index(self.mask_index[i],
                                                            cmask.mask_index[i])
        return self.mask_index

    def __repr__(self):
        return 'mask_index: {}'.format(self.mask_index)

    def eq_on_dim(self, other, dim):
        assert isinstance(other, CoarseMask)
        if self.mask_index[dim] is None and other.mask_index[dim] is None:
            return True
        elif isinstance(self.mask_index[dim], torch.Tensor) \
                and isinstance(other.mask_index[dim], torch.Tensor):
            return torch.equal(self.mask_index[dim], other.mask_index[dim])
        else:
            return False

    def __eq__(self, other):
        assert isinstance(other, CoarseMask)
        if len(self.mask_index) != len(other.mask_index):
            return False
        for i in range(len(self.mask_index)):
            if not self.eq_on_dim(other, i):
                return False
        return True

    def __lt__(self, other):
        """
        Judge if the mask is a subset of another CoarseMask.
        """
        assert isinstance(other, CoarseMask)
        for dim, _ in enumerate(self.mask_index):
            # if self has more dimensions
            if dim >= len(other.mask_index):
                return False
            if self.mask_index[dim] is None:
                # if no mask on this dimension, then we have less
                # masks then the other CoraseMask.
                continue
            elif other.mask_index[dim] is None:
                return False
            else:
                s1 = set(self.mask_index[dim].tolist())
                s2 = set(other.mask_index[dim].tolist())
                if not s1 < s2:
                    return False
        return True

    def __le__(self, other):
        """
        Return if self's mask is less or equal to other's mask.
        """
        assert isinstance(other, CoarseMask)
        if self.__lt__(other) or self.__eq__(other):
            return True
        return False

    def __ne__(self, other):
        return not self.__eq__(other)


class ModuleMasks:
    """
    The masks of a module, including the masks for weights, inputs, output
    """

    def __init__(self, module_name, module=None):
        """
        Parameters
        ----------
        module_name : str
            The name of the module or function
        """
        self.module_name = module_name
        self.module = module
        self.param_masks = dict()
        self.input_mask = None
        self.output_mask = None

    def set_param_masks(self, name, mask):
        """
        Parameters
        ----------
        name : str
            The name of the weight
        mask : CoarseMask
            The mask for this weight
        """
        self.param_masks[name] = mask

    def set_input_mask(self, mask):
        """
        Parameters
        ----------
        mask : CoarseMask
            The mask for input
        """
        self.input_mask = mask

    def set_output_mask(self, mask):
        """
        Parameters
        ----------
        mask : CoarseMask
            The mask for output
        """
        self.output_mask = mask

    def __repr__(self):
        return 'module_name: {}, input_mask: {}, output_mask: {}, param_masks: {}'.format(
            self.module_name, self.input_mask, self.output_mask, self.param_masks
        )


"""
Infer input and output shape of a module/function from its weight mask
"""
infer_from_mask = {
    'BatchNorm2d': lambda module_masks, mask: batchnorm2d_mask(module_masks, mask),
    'Conv2d': lambda module_masks, mask: conv2d_mask(module_masks, mask),
    'ConvTranspose2d': lambda module_masks, mask: convtranspose2d_mask(module_masks, mask),
    'Linear': lambda module_masks, mask, shape: linear_mask(module_masks, mask, shape)
}

"""
Infer output and weight shape of a module/function from its input shape
"""
infer_from_inshape = {
    'ReLU': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'ReLU6': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'PReLU': lambda module_masks, mask: prelu_inshape(module_masks, mask),
    'Sigmoid': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'aten::relu': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'aten::tanh': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'aten::tanh_': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'aten::hardtanh': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'aten::hardtanh_': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'aten::relu_': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'aten::sigmoid': lambda module_masks, mask: relu_inshape(module_masks, mask),
    'Conv2d': lambda module_masks, mask: conv2d_inshape(module_masks, mask),
    'ConvTranspose2d': lambda module_masks, mask: convtranspose2d_inshape(module_masks, mask),
    'MaxPool2d': lambda module_masks, mask: maxpool2d_inshape(module_masks, mask),
    'aten::max_pool2d': lambda module_masks, mask: maxpool2d_inshape(module_masks, mask),
    'aten::avg_pool2d': lambda module_masks, mask: maxpool2d_inshape(module_masks, mask),
    'aten::adaptive_avg_pool2d': lambda module_masks, mask: maxpool2d_inshape(module_masks, mask),
    'AvgPool2d': lambda module_masks, mask: maxpool2d_inshape(module_masks, mask),
    'AdaptiveAvgPool2d': lambda module_masks, mask: maxpool2d_inshape(module_masks, mask),
    'aten::size': lambda module_masks, mask: size_inshape(module_masks, mask),
    'aten::view': lambda module_masks, mask, shape: view_inshape(module_masks, mask, shape),
    'aten::reshape': lambda module_masks, mask, shape: view_inshape(module_masks, mask, shape),
    # support only start_dim=1
    'aten::flatten': lambda module_masks, mask, shape: view_inshape(module_masks, mask, shape),
    'Linear': lambda module_masks, mask: linear_inshape(module_masks, mask),
    'BatchNorm2d': lambda module_masks, mask: batchnorm2d_inshape(module_masks, mask),
    'aten::add_': lambda module_masks, mask: add_inshape(module_masks, mask),
    'aten::add': lambda module_mask, mask: add_inshape(module_mask, mask),
    # mul has the similar behaviour with add, they both request
    # the input tesors to have the same shape
    'aten::mul': lambda module_mask, mask: add_inshape(module_mask, mask),
    'aten::mul_': lambda module_mask, mask: add_inshape(module_mask, mask),
    'aten::cat': lambda module_mask, mask, cat_info, last_visited: cat_inshape(module_mask, mask, cat_info, last_visited),
    'aten::mean': lambda module_masks, mask, shape: mean_inshape(module_masks, mask, shape),
    'Dropout': lambda module_masks, mask: dropout_inshape(module_masks, mask),
    'Dropout2d': lambda module_masks, mask: dropout_inshape(module_masks, mask),
    'aten::dropout': lambda module_masks, mask: dropout_inshape(module_masks, mask),
    'aten::detach': lambda module_masks, mask: dropout_inshape(module_masks, mask)
}

"""
Infer input and weight shape of a module/function from its output shape
"""
infer_from_outshape = {
    'Conv2d': lambda module_masks, mask: conv2d_outshape(module_masks, mask),
    'ConvTranspose2d': lambda module_masks, mask: convtranspose2d_outshape(module_masks, mask),
    'BatchNorm2d': lambda module_masks, mask: batchnorm2d_outshape(module_masks, mask),

    'MaxPool2d': lambda module_masks, mask: maxpool2d_outshape(module_masks, mask),
    'aten::max_pool2d': lambda module_masks, mask: maxpool2d_outshape(module_masks, mask),
    'aten::avg_pool2d': lambda module_masks, mask: maxpool2d_outshape(module_masks, mask),
    'aten::adaptive_avg_pool2d': lambda module_masks, mask: maxpool2d_outshape(module_masks, mask),
    'AvgPool2d': lambda module_masks, mask: maxpool2d_outshape(module_masks, mask),
    'AdaptiveAvgPool2d': lambda module_masks, mask: maxpool2d_outshape(module_masks, mask),

    'ReLU': lambda module_masks, mask: relu_outshape(module_masks, mask),
    'PReLU': lambda module_masks, mask: prelu_outshape(module_masks, mask),
    'ReLU6': lambda module_masks, mask: relu_outshape(module_masks, mask),
    'aten::relu': lambda module_masks, mask: relu_outshape(module_masks, mask),
    'aten::tanh': lambda module_masks, mask: relu_outshape(module_masks, mask),
    'aten::tanh_': lambda module_masks, mask: relu_outshape(module_masks, mask),
    'aten::hardtanh': lambda module_masks, mask: relu_outshape(module_masks, mask),
    'aten::hardtanh_': lambda module_masks, mask: relu_outshape(module_masks, mask),
    'aten::relu_': lambda module_masks, mask: relu_outshape(module_masks, mask),

    'aten::add_': lambda module_masks, mask: add_outshape(module_masks, mask),
    'aten::add': lambda module_mask, mask: add_outshape(module_mask, mask),
    'aten::flatten': lambda module_mask, mask, shape: view_outshape(module_mask, mask, shape),
    'aten::view': lambda module_masks, mask, shape: view_outshape(module_masks, mask, shape),
    'aten::reshape': lambda module_masks, mask, shape: view_outshape(module_masks, mask, shape),
    'aten::mean': lambda module_masks, mask, shape: mean_outshape(module_masks, mask, shape),
    'Dropout': lambda module_masks, mask: dropout_outshape(module_masks, mask),
    'Dropout2d': lambda module_masks, mask: dropout_outshape(module_masks, mask),
    'aten::dropout': lambda module_masks, mask: dropout_outshape(module_masks, mask),
    'aten::detach': lambda module_masks, mask: dropout_outshape(module_masks, mask)
}


def dropout_inshape(module_masks, mask):
    if module_masks.input_mask is None:
        module_masks.set_input_mask(mask)
        module_masks.set_output_mask(mask)
        return module_masks.output_mask
    # if alreay visited
    assert module_masks.input_mask <= mask
    # It should be the same, we pass the masks by the reference(not the value),
    # so they acutually are two references of the same object(mask,
    # module_masks.input_mask). So we should continue pass the mask
    # to the following nodes even module_masks.input_mask == mask.
    # if pass the mask by copy.deepcopy(), then we can stop when
    # module_masks.input_mask == mask.
    # if module_masks.input_mask == mask:
    #     return None
    module_masks.set_input_mask(mask)
    module_masks.set_output_mask(mask)
    return module_masks.output_mask


def dropout_outshape(module_masks, mask):
    if module_masks.output_mask is None:
        module_masks.set_output_mask(mask)
        module_masks.set_input_mask(mask)
        return module_masks.input_mask
    # if alreay visited
    assert all(module_masks.output_mask.mask_index[1] == mask.mask_index[1])

    return module_masks.output_mask


def cat_inshape(module_masks, mask, cat_info, last_visited):
    """
    Inference the output mask of the cat operation from the
    input mask.

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the Conv2d
    mask : CoarseMask
        The mask of its input tensor
    cat_info: dict
        Dict object that records the necessary information
        of cat operation, such as the order of the input
        tensors.
    last_visited: str
        The unique_name of the last visited node group.

    Returns
    -------
    CoarseMask
        The mask of its output tensor

    """
    assert isinstance(mask, CoarseMask)
    out_shape = cat_info['out_shape']
    cat_dim = cat_info['cat_dim']
    in_order = cat_info['in_order']
    in_shape = cat_info['in_shape']
    if module_masks.output_mask is None:
        # First visit to this cat node
        # initialize the mask based on
        # the number of the output channel.
        output_mask = CoarseMask(num_dim=len(out_shape))
        for dim, _ in enumerate(out_shape):
            if dim == cat_dim:
                if mask.mask_index[dim] is None:
                    continue
                device = mask.mask_index[dim].device
                # calculate the offset of the mask
                pos = in_order.index(last_visited)
                offsets = [in_shape[i][cat_dim]
                           for i, _ in enumerate(in_shape)]
                offset = 0
                for i in range(pos):
                    offset += offsets[i]
                _tmp_mask = (mask.mask_index[dim] + offset).to(device)
                output_mask.mask_index[dim] = _tmp_mask
            else:
                # directly copy the mask
                if mask.mask_index[dim] is not None:
                    output_mask.mask_index[dim] = mask.mask_index[dim].data.clone(
                    )
        module_masks.set_output_mask(output_mask)

        return module_masks.output_mask
    # If this cat node is already visited, we need
    # validating if the mask is legel, for cat operation,
    # the mask on the 'cat_dim' dimension should be stitched
    # together. In the other dimensions, the mask should be
    # the same, else the mask is not legal.
    for dim, _ in enumerate(out_shape):
        if dim == cat_dim:
            if mask.mask_index[dim] is None:
                continue
            pos = in_order.index(last_visited)
            offsets = [in_shape[i][cat_dim] for i, _ in enumerate(in_shape)]
            offset = 0
            for i in range(pos):
                offset += offsets[i]
            device = mask.mask_index[dim].device
            new_mask = mask.mask_index[dim] + offset
            module_masks.output_mask.mask_index[dim] = CoarseMask.merge_index(
                module_masks.output_mask.mask_index[dim], new_mask).to(device)
        else:
            assert module_masks.output_mask.eq_on_dim(mask, dim)

    return module_masks.output_mask


def add_inshape(module_masks, mask):
    """
    Inference the output mask of the add operation from the
    input mask.
    """
    assert isinstance(mask, CoarseMask)
    if module_masks.input_mask is None:
        module_masks.set_input_mask(mask)
        module_masks.set_output_mask(mask)
        # module_masks.input_mask = mask
        return mask
    # If alreay visited, validate if have the conflict
    # if the mask is different with previous input_mask
    # then there is a mask confilct.
    if mask != module_masks.input_mask:
        raise Exception('Mask conflict happenes!')
    return None


def add_outshape(module_masks, mask):
    """
    Inference the input mask of the add operation from the
    output mask.
    """
    assert isinstance(mask, CoarseMask)

    if module_masks.output_mask is None:
        module_masks.set_output_mask(mask)
        module_masks.set_input_mask(mask)
        return mask
    else:
        assert all(
            module_masks.output_mask.mask_index[1] == mask.mask_index[1])
    return mask


def batchnorm2d_inshape(module_masks, mask):
    """
    We assume only the second dimension has coarse grained mask

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the batchnorm2d
    mask : CoarseMask
        The mask of its input tensor

    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    assert mask.mask_index[2] is None
    assert mask.mask_index[3] is None
    module_masks.set_input_mask(mask)
    module_masks.set_output_mask(mask)
    weight_cmask = CoarseMask(num_dim=1)
    weight_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
    module_masks.set_param_masks('weight', weight_cmask)
    module_masks.set_param_masks('bias', weight_cmask)
    return mask


def batchnorm2d_outshape(module_masks, mask):
    """
    We assume only the second dimension has coarse grained mask

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the batchnorm2d
    mask : CoarseMask
        The mask of its input tensor

    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    assert len(mask.mask_index) in [2, 4]
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    module_masks.set_input_mask(mask)
    module_masks.set_output_mask(mask)
    weight_cmask = CoarseMask(num_dim=1)
    weight_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
    module_masks.set_param_masks('weight', weight_cmask)
    module_masks.set_param_masks('bias', weight_cmask)
    return mask


def linear_inshape(module_masks, mask):
    """
    Coarse grained input mask does not change the shape of weights and output tensor

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the linear
    mask : CoarseMask
        The mask of its input tensor

    Returns
    -------
    CoarseMask
        The mask of its output tensor, ```None``` means shape of output tensor is not changed
    """
    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[0] is None
    if module_masks.input_mask is not None:
        assert module_masks.input_mask <= mask
    module_masks.set_input_mask(mask)
    return None


def view_inshape(module_masks, mask, shape):
    """
    This is a limited support

    TODO: consider replace tensor.view with nn.Flatten, because tensor.view is not
    included in module, thus, cannot be replaced by our framework.

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the ```view``` op
    mask : CoarseMask
        The mask of its input tensor
    shape : dict
        Original shape of its input and output tensors

    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    # NOTE: the case constrained by the following four asserts
    assert shape['in_shape'][0] == shape['out_shape'][0]
    assert len(shape['in_shape']) == 4
    assert len(shape['out_shape']) == 2
    assert shape['out_shape'][1] == shape['in_shape'][1] * \
        shape['in_shape'][2]*shape['in_shape'][3]

    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    assert mask.mask_index[2] is None
    assert mask.mask_index[3] is None
    # due to the cat operation, the same node may be
    # accessed more than once
    if module_masks.input_mask is not None:
        assert module_masks.input_mask <= mask
    module_masks.set_input_mask(mask)
    output_cmask = CoarseMask(num_dim=2)
    index = []
    step_size = shape['in_shape'][2] * shape['in_shape'][3]
    for loc in mask.mask_index[1]:
        index.extend([loc * step_size + i for i in range(step_size)])
    output_cmask.add_index_mask(dim=1, index=torch.tensor(index).to(mask.mask_index[1].device))  # pylint: disable=not-callable
    module_masks.set_output_mask(output_cmask)
    return output_cmask


def view_outshape(module_masks, mask, shape):
    """
    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the ```view``` op
    mask : CoarseMask
        The mask of its output tensor
    shape : dict
        Original shape of its input and output tensors
    Returns
    -------
    CoarseMask
        The mask of its input tensor
    """
    # NOTE: the case constrained by the following four asserts
    assert shape['in_shape'][0] == shape['out_shape'][0]
    assert len(shape['in_shape']) == 4
    assert len(shape['out_shape']) == 2
    assert shape['out_shape'][1] == shape['in_shape'][1] * \
        shape['in_shape'][2]*shape['in_shape'][3]

    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None

    module_masks.set_output_mask(mask)
    input_cmask = CoarseMask(num_dim=4)
    index = set()
    step_size = shape['in_shape'][2] * shape['in_shape'][3]
    for loc in mask.mask_index[1]:
        index.add(loc // step_size)
    index = sorted(list(index))
    input_cmask.add_index_mask(dim=1, index=torch.tensor(index).to(mask.mask_index[1].device))  # pylint: disable=not-callable
    module_masks.set_input_mask(input_cmask)

    return input_cmask


def size_inshape(module_masks, mask):
    """
    No need to do anything for this ```size``` op
    """
    return None


def mean_inshape(module_masks, mask, shape):
    """
    Similar to view operation, currently mask inference only supports
    the mean operation on the 3rd and 4th dimensions.
    """
    assert shape['in_shape'][0] == shape['out_shape'][0]
    assert shape['out_shape'][1] == shape['in_shape'][1]
    assert len(shape['in_shape']) == 4
    assert len(shape['out_shape']) == 2

    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    assert mask.mask_index[2] is None
    assert mask.mask_index[3] is None
    module_masks.set_input_mask(mask)

    output_cmask = CoarseMask(num_dim=2)
    output_cmask.add_index_mask(dim=1, index=mask.mask_index[1])
    module_masks.set_output_mask(output_cmask)
    return output_cmask


def mean_outshape(module_masks, mask, shape):
    """
    Similar to view operation, currently mask inference only supports
    the mean operation on the 3rd and 4th dimensions.
    """
    assert shape['in_shape'][0] == shape['out_shape'][0]
    assert shape['out_shape'][1] == shape['in_shape'][1]
    assert len(shape['in_shape']) == 4
    assert len(shape['out_shape']) == 2

    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    module_masks.set_output_mask(mask)

    input_cmask = CoarseMask(num_dim=4)
    input_cmask.add_index_mask(dim=1, index=mask.mask_index[1])
    module_masks.set_input_mask(input_cmask)
    return input_cmask


def maxpool2d_inshape(module_masks, mask):
    """
    Assume only the second dimension is masked

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the maxpool2d
    mask : CoarseMask
        The mask of its input tensor

    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    assert mask.mask_index[2] is None
    assert mask.mask_index[3] is None
    if module_masks.input_mask is not None:
        assert module_masks.input_mask <= mask
    # assert module_masks.input_mask is None
    module_masks.set_input_mask(mask)
    module_masks.set_output_mask(mask)
    return mask


def maxpool2d_outshape(module_masks, mask):
    """
    Assume only the second dimension is masked

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the maxpool2d
    mask : CoarseMask
        The mask of its input tensor

    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None

    module_masks.set_input_mask(mask)
    module_masks.set_output_mask(mask)
    return mask

def prelu_inshape(module_masks, mask):
    """
    We assume only the second dimension has coarse grained mask

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the PReLU
    mask : CoarseMask
        The mask of its input tensor

    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    assert mask.mask_index[2] is None
    assert mask.mask_index[3] is None
    module_masks.set_input_mask(mask)
    module_masks.set_output_mask(mask)
    weight_cmask = CoarseMask(num_dim=1)
    weight_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
    module_masks.set_param_masks('weight', weight_cmask)
    return mask

def prelu_outshape(module_masks, mask):
    """
    We assume only the second dimension has coarse grained mask

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the PReLU
    mask : CoarseMask
        The mask of its input tensor

    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    assert mask.mask_index[2] is None
    assert mask.mask_index[3] is None

    weight_cmask = CoarseMask(num_dim=4)
    weight_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
    module_masks.set_param_masks('weight', weight_cmask)

    return mask


def relu_inshape(module_masks, mask):
    """
    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the relu
    mask : CoarseMask
        The mask of its input tensor

    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    if module_masks.input_mask is not None:
        # mask conflict should be solved before speedup
        assert module_masks.input_mask <= mask
    # assert module_masks.input_mask is None, "A relu op can only be processed once"
    module_masks.set_input_mask(mask)
    module_masks.set_output_mask(mask)
    return mask


def relu_outshape(module_masks, mask):
    """
    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the relu
    mask : CoarseMask
        The mask of its input tensor

    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    if module_masks.output_mask is not None:
        # mask conflict should be solved before speedup
        assert all(
            module_masks.output_mask.mask_index[1] == mask.mask_index[1])
    module_masks.set_input_mask(mask)
    module_masks.set_output_mask(mask)
    return mask


def batchnorm2d_mask(module_masks, mask):
    """
    Infer input and output shape from weight mask
    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the batchnorm2d
    mask : dict
        The mask of its weights, from the user provided mask file
    Returns
    -------
    CoarseMask, CoarseMask
        The mask of its input tensor, the mask of its output tensor
    """
    assert 'weight' in mask and 'bias' in mask
    sum_mask = mask['weight'] + mask['bias']
    nonzero_index = torch.nonzero(sum_mask, as_tuple=True)[0]
    # infer shape of parameters
    param_cmask = CoarseMask(num_dim=1)
    param_cmask.add_index_mask(dim=0, index=nonzero_index)
    module_masks.set_param_masks('weight', param_cmask)
    module_masks.set_param_masks('bias', param_cmask)
    # infer shape of input tensor
    input_cmask = CoarseMask(num_dim=4)
    input_cmask.add_index_mask(dim=1,
                               index=torch.nonzero(mask['weight'], as_tuple=True)[0])
    module_masks.set_input_mask(input_cmask)
    # infer shape of output tensor
    output_cmask = CoarseMask(num_dim=4)
    output_cmask.add_index_mask(dim=1, index=nonzero_index)
    module_masks.set_output_mask(output_cmask)
    return input_cmask, output_cmask


def linear_mask(module_masks, mask, shape):
    """
    Infer input and output shape from weight mask with limitations:
    Only support infer input mask

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the Linear
    mask : dict
        The mask of its weights, from the user provided mask file
    shape: dict
        Shape of its input and output tensors
    Returns
    -------
    CoarseMask, CoarseMask
        The mask of its input tensor, the mask of its output tensor
    """

    assert 'weight' in mask
    num_input_dim = len(shape['in_shape'])

    # Input data of Linear module can have multiple dimensions.
    # here we only support infer coarse mask on the first dimension (dimension 0)
    nonzero_index = torch.nonzero(mask['weight'].sum(0), as_tuple=True)[0]

    # infer shape of input tensor
    input_cmask = CoarseMask(num_dim=num_input_dim)
    input_cmask.add_index_mask(dim=num_input_dim-1, index=nonzero_index)

    module_masks.set_input_mask(input_cmask)
    return input_cmask, None


def conv2d_mask(module_masks, mask):
    """
    Infer input and output shape from weight mask

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the conv2d
    mask : dict
        The mask of its weights, from the user provided mask file

    Returns
    -------
    CoarseMask, CoarseMask
        The mask of its input tensor, the mask of its output tensor
    """
    def convert_to_coarse_mask(mask, dim=0):
        """
        Parameters
        ----------
        mask : dict
            Weight mask from user provided mask file
        dim: int
            0: filter pruning
            1: channel pruning

        Returns
        -------
        LongTensor, CoarseMask, CoarseMask
            Index of the masked dimension, weight mask, bias mask
        """
        assert 'weight' in mask
        assert isinstance(mask['weight'], torch.Tensor)
        assert dim in [0, 1]

        weight_mask = mask['weight']

        sum_idx = (1, 2, 3) if dim == 0 else (0, 2, 3)
        index = torch.nonzero(weight_mask.abs().sum(
            sum_idx) != 0, as_tuple=True)[0]

        index = index.long().to(weight_mask.device)
        weight_cmask = CoarseMask(num_dim=4)
        weight_cmask.add_index_mask(dim=dim, index=index)
        bias_cmask = None
        if dim == 0 and 'bias' in mask and mask['bias'] is not None:
            bias_index = torch.nonzero(mask['bias'], as_tuple=True)[0]
            assert torch.all(torch.eq(index, bias_index)), \
                "bias mask should be consistent with weight mask"
            bias_cmask = CoarseMask(num_dim=1)
            bias_cmask.add_index_mask(dim=0, index=bias_index)
        return index, weight_cmask, bias_cmask

    index, weight_cmask, bias_cmask = convert_to_coarse_mask(
        mask, dim=conv_prune_dim)

    if index is None:
        # TODO: fine grained mask speedup
        return None, None
    # deal with coarse grain mask
    # mask conflict should be solved by fix_mask_conflict before speedup
    if 'weight' in module_masks.param_masks:
        assert module_masks.param_masks['weight'] == weight_cmask
    else:
        module_masks.set_param_masks('weight', weight_cmask)
        if conv_prune_dim == 0:
            module_masks.set_param_masks('bias', bias_cmask)

    io_cmask = CoarseMask(num_dim=4)
    io_cmask.add_index_mask(dim=1, index=index)

    if conv_prune_dim == 0:
        if module_masks.output_mask is None:
            module_masks.set_output_mask(io_cmask)
        else:
            assert module_masks.output_mask == io_cmask
        return None, module_masks.output_mask
    else:
        if module_masks.input_mask is None:
            module_masks.set_input_mask(io_cmask)
        else:
            assert module_masks.input_mask == io_cmask
        return module_masks.input_mask, None


def conv2d_inshape(module_masks, mask):
    """
    Shape change of input tensor does not affect the shape of its output tensor
    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the conv2d
    mask : CoarseMask
        The mask of its input tensor
    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    if module_masks.input_mask is None:
        module_masks.set_input_mask(mask)
    else:
        # the same conv layer may be accessed more
        # than once, such as a concat operation.
        # mask conflict should be solved by fix_mask_conflict before speedup

        assert module_masks.input_mask == mask

    # shape changes pass through depths wise conv layers
    m = module_masks.module
    if m.in_channels == m.out_channels == m.groups:
        module_masks.output_mask = mask
        module_masks.input_mask = mask
        return mask
    return None


def conv2d_outshape(module_masks, mask):
    """
    Assume only the second dimension is masked

    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the conv2d
    mask : CoarseMask
        The mask of its output tensor

    Returns
    -------
    CoarseMask
        The mask of its input tensor
    """
    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    assert mask.mask_index[2] is None
    assert mask.mask_index[3] is None

    if module_masks.output_mask is None:
        module_masks.output_mask = mask
    else:
        # mask conflict should be solved by fix_mask_conflict before speedup
        # mask and module_masks.output_mask may have different number of dimensions
        # since they could be passed by linear or conv2d
        assert all(
            module_masks.output_mask.mask_index[1] == mask.mask_index[1])

    weight_cmask = CoarseMask(num_dim=4)
    weight_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
    bias_cmask = CoarseMask(num_dim=1)
    bias_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
    module_masks.set_param_masks('weight', weight_cmask)
    module_masks.set_param_masks('bias', bias_cmask)

    # shape changes pass through depths wise conv layers
    m = module_masks.module
    if m.in_channels == m.out_channels == m.groups:
        module_masks.output_mask = mask
        module_masks.input_mask = mask
        return mask
    return None


def convtranspose2d_mask(module_masks, mask):
    # TODO support the Convtranspose2d Pruning for the L1FilterPruner
    raise Exception(
        "Current Filter pruner cannot prune the ConvTranspose2d, will support pruning ConvTranspose2d later")


def convtranspose2d_inshape(module_masks, mask):
    """
    Shape change of input tensor does not affect the shape of its output tensor
    Parameters
    ----------
    module_masks : ModuleMasks
        The ModuleMasks instance of the conv2d
    mask : CoarseMask
        The mask of its input tensor
    Returns
    -------
    CoarseMask
        The mask of its output tensor
    """
    assert isinstance(mask, CoarseMask)
    if module_masks.input_mask is None:
        module_masks.set_input_mask(mask)
    else:
        # the same conv layer may be accessed more
        # than once, such as a concat operation.
        # mask conflict should be solved by fix_mask_conflict before speedup
        assert module_masks.input_mask == mask

    # shape changes pass through depths wise conv layers
    m = module_masks.module
    if m.in_channels == m.out_channels == m.groups:
        module_masks.output_mask = mask
        module_masks.input_mask = mask
        return mask
    return None


def convtranspose2d_outshape(module_masks, mask):
    assert isinstance(mask, CoarseMask)
    assert mask.mask_index[1] is not None
    assert mask.mask_index[0] is None
    assert mask.mask_index[2] is None
    assert mask.mask_index[3] is None

    if module_masks.output_mask is None:
        module_masks.output_mask = mask
    else:
        # mask conflict should be solved by fix_mask_conflict before speedup
        # mask and module_masks.output_mask may have different number of dimensions
        # since they could be passed by linear or conv2d
        assert all(
            module_masks.output_mask.mask_index[1] == mask.mask_index[1])

    weight_cmask = CoarseMask(num_dim=4)
    # Note the memory layout of Convtranspose2d is C_in, C_out, k1, k2
    weight_cmask.add_index_mask(dim=1, index=mask.mask_index[1])
    bias_cmask = CoarseMask(num_dim=1)
    bias_cmask.add_index_mask(dim=0, index=mask.mask_index[1])
    module_masks.set_param_masks('weight', weight_cmask)
    module_masks.set_param_masks('bias', bias_cmask)

    # shape changes pass through depths wise conv layers
    m = module_masks.module
    if m.in_channels == m.out_channels == m.groups:
        module_masks.output_mask = mask
        module_masks.input_mask = mask
        return mask
    return None
